Khan 2018 Mouse Lens Re-analysis¶

Lens changes with age groups notebook¶

E15 -vs E18+P0 and E18+P0 -vs- P3+P6 in pairwise comparisons¶

TMM normalization is used after IRS here¶

Prepared by Phil Wilmarth, PSR Core, OHSU¶

December 26, 2023¶


  • Overview
  • Data loading
  • E15 versus mid ages
  • Mid ages versus older ages
  • Summary

Overview¶

This is a three plex experiment using 10-plex TMT with no reference channels per plex. Each plex is a set of biological replicates of developing mouse lens: E15, E18, P0, P3, P6, P9 (six ages, 3 day intervals). Data were downloaded from PRIDE (PXD006381) and processed with the PAW Pipeline (Wilmarth 2009) and IRS normalized using the average of each plex as a mock reference channel (Plubell 2017). This is the publication:

Khan, S.Y., Ali, M., Kabir, F., Renuse, S., Na, C.H., Talbot, C.C., Hackett, S.F. and Riazuddin, S.A., 2018. Proteome profiling of developing murine lens through mass spectrometry. Investigative Ophthalmology & Visual Science, 59(1), pp.100-107.

Experimental Groups:¶

Age Description Biological Replicates Pooled Mice Lenses
E15 Embryonic day 15 3 23 embryos
E18 Embryonic day 18 3 10 embryos
P0 Newborn 3 8 pups
P3 3-day old 3 8 pups
P6 6-day old 3 8 pups
P9 9-day old 3 8 pups

Sample Key:¶

Channel Plex 1 Plex 2 Plex 3
126C E15 E15 E15
127N E18 E18 E18
127C P0 P0 P0
128N P3 P3 P3
128C P6 P6 P6
129N P9 P9 P9
129C Mix? Mix? Mix?
130N unused unused unused
130C unused unused unused
131N unused unused unused

This notebook will compare lenses of different ages¶

There were three plexes with biological replicates of the 6 ages. There was no pooled internal reference standard. The plex average intensity for each protein was used as a mock reference intensity vector. IRS was used to put all three plexes (18 samples) on a common intensity scale.

Based on MDS clustering and cumulative abundance distribution functions, there appear to be three age groups that may capture the main developmental changes (E15 versus E18+P0 and E18+P0 versus P3+P6). Statistical analysis is done with edgeR (Robinson 2010A) exact test after TMM normalization (Robinson 2010B).

References¶

Wilmarth, P.A., Riviere, M.A. and David, L.L., 2009. Techniques for accurate protein identification in shotgun proteomic studies of human, mouse, bovine, and chicken lenses. Journal of ocular biology, diseases, and informatics, 2(4), pp.223-234.

Plubell, D.L., Wilmarth, P.A., Zhao, Y., Fenton, A.M., Minnier, J., Reddy, A.P., Klimek, J., Yang, X., David, L.L. and Pamir, N., 2017. Extended multiplexing of tandem mass tags (TMT) labeling reveals age and high fat diet specific proteome changes in mouse epididymal adipose tissue. Molecular & Cellular Proteomics, 16(5), pp.873-890.

Robinson, M.D., McCarthy, D.J. and Smyth, G.K., 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. bioinformatics, 26(1), pp.139-140.

Robinson, M.D. and Oshlack, A., 2010. A scaling normalization method for differential expression analysis of RNA-seq data. Genome biology, 11(3), pp.1-9.


Load the necessary R libraries¶

In [1]:
# library imports
library(tidyverse)
library(scales)
library(limma)
library(edgeR)
library(psych)
Warning message:
“replacing previous import ‘lifecycle::last_warnings’ by ‘rlang::last_warnings’ when loading ‘tibble’”
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

✔ ggplot2 3.3.2     ✔ purrr   0.3.4
✔ tibble  3.0.3     ✔ dplyr   1.0.2
✔ tidyr   1.1.1     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.5.0

── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()


Attaching package: ‘scales’


The following object is masked from ‘package:purrr’:

    discard


The following object is masked from ‘package:readr’:

    col_factor



Attaching package: ‘psych’


The following objects are masked from ‘package:scales’:

    alpha, rescale


The following objects are masked from ‘package:ggplot2’:

    %+%, alpha


Define common functions for notebook use¶

In [2]:
# ================== TMM normalization from DGEList object =====================
apply_tmm_factors <- function(y, color = NULL, plot = TRUE, table = TRUE, title = "TMM Normalized data") {
    # computes the tmm normalized data from the DGEList object
        # y - DGEList object
        # color - color vector for boxplots
        # plot - flag to show boxplot or not
        # table - flag to print norm factor tables or not
        # title - main title for boxplots
        # returns a dataframe with normalized intensities
    
    # compute and print "Sample loading" normalization factors
    lib_facs <- mean(y$samples$lib.size) / y$samples$lib.size
    if(table == TRUE) {
        cat("\nLibrary size factors:\n", 
            sprintf("%-5s -> %f\n", colnames(y$counts), lib_facs))
    }
    
    # compute and print TMM normalization factors
    tmm_facs <- 1/y$samples$norm.factors
    if(table == TRUE) {
        cat("\nTrimmed mean of M-values (TMM) factors:\n", 
            sprintf("%-5s -> %f\n", colnames(y$counts), tmm_facs))
    }
    
    # compute and print the final correction factors
    norm_facs <- lib_facs * tmm_facs
    if(table == TRUE) {
        cat("\nCombined (lib size and TMM) normalization factors:\n", 
            sprintf("%-5s -> %f\n", colnames(y$counts), norm_facs))
    }

    # compute the normalized data as a new data frame
    tmt_tmm <- as.data.frame(sweep(y$counts, 2, norm_facs, FUN = "*"))
    colnames(tmt_tmm) <- str_c(colnames(y$counts), "_tmm")
    
    # visualize results and return data frame
    if(plot == TRUE) {
        boxplot(log10(tmt_tmm), col = color, notch = TRUE, main = title)
    }
    tmt_tmm
}

# ============ runs an edgeR exact pairwise test ========================
pairwise_test <- function(delist, pair, p.value) {
    # runs an edgeR exact test and returns results
    et <- exactTest(delist, pair = pair)
    tt <- topTags(et, n = Inf, sort.by = "none")
    
    # print top tags
    cat("\n")
    print(topTags(et)$table, digits = 4)
    cat("\n")
    
    # count candidates
    print(summary(decideTestsDGE(et, p.value = p.value)))

    # see candidates on an MA plot
    plotMD(et, p.value = p.value)
    abline(h = c(-1, 1), col = "black")
    
    # check the p-value distribution
    pval <- ggplot(tt$table, aes(PValue)) + 
      geom_histogram(bins = 100, fill = "white", color = "black") + 
      geom_hline(yintercept = mean(hist(et$table$PValue, breaks = 100, 
                                    plot = FALSE)$counts[26:100])) +
      ggtitle("p-value distribution")
    print(pval) # this makes the plot show up
    
    tt # return top-tags object
}

# ================= reformat edgeR test results ================================
collect_results <- function(df, tt, x, xlab, y, ylab) {
    # Computes new columns and extracts some columns to make results frame
        # df - data in data.frame
        # tt - top tags table from edgeR test
        # x - columns for first condition
        # xlab - label for x
        # y - columns for second condition
        # ylab - label for y
        # returns a new dataframe
    
    # condition average vectors
    ave_x <- rowMeans(df[x])
    ave_y <- rowMeans(df[y])
    
    # FC, direction, candidates
    fc <- ifelse(ave_y > ave_x, (ave_y / ave_x), (-1 * ave_x / ave_y))
    direction <- ifelse(ave_y > ave_x, "up", "down")
    candidate <- cut(tt$FDR, breaks = c(-Inf, 0.01, 0.05, 0.10, 1.0), 
                     labels = c("high", "med", "low", "no"))
    
    # make data frame
    temp <- cbind(df[c(x, y)], data.frame(logFC = tt$logFC, FC = fc, 
                                          PValue = tt$PValue, FDR = tt$FDR, 
                                          ave_x = ave_x, ave_y = ave_y, 
                                          direction = direction, candidate = candidate, 
                                          Acc = tt$genes)) 
    
    # fix column headers for averages
    names(temp)[names(temp) %in% c("ave_x", "ave_y")]  <- str_c("ave_", c(xlab, ylab))    
    
    temp # return the data frame
}

# =============== log2_fold_changes ================================================
log2_fold_changes <- function(results, left, right, title) {
    # Makes log2 fold change plots by candidate
        # results - results data frame
        # left - lower FC log2 limit
        # right - upper FC log2 limit
        # title - plot title
    
    # see how many candidates by category
    cat("\n")
    print(results %>% count(candidate))
    cat("\n")

    # plot log2 fold-changes by category
    fc <- ggplot(results, aes(x = logFC, fill = candidate)) +
      geom_histogram(binwidth=0.1, color = "black") +
      facet_wrap(~candidate) +
      coord_cartesian(xlim = c(-left, right)) +
      ggtitle(title)
    print(fc)
}

# ========== Setup for MA and volcano plots ====================================
transform <- function(results, x, y) {
    # Make data frame with some transformed columns
        # results - results data frame
        # x - columns for x condition
        # y - columns for y condition
        # return new data frame
    df <- data.frame(log10((results[x] + results[y])/2), 
                     log2(results[y] / results[x]), 
                     results$candidate,
                     -log10(results$FDR))
    colnames(df) <- c("A", "M", "candidate", "P")
    
    df # return the data frame
}

# ========== MA plots using ggplot =============================================
MA_plots <- function(results, x, y, title) {
    # makes MA-plot DE candidate ggplots
        # results - data frame with edgeR results and some condition average columns
        # x - string for x-axis column
        # y - string for y-axis column
        # title - title string to use in plots
        # returns a list of plots 
    
    # uses transformed data
    temp <- transform(results, x, y)
    
    # 2-fold change lines
    ma_lines <- list(geom_hline(yintercept = 0.0, color = "black"),
                     geom_hline(yintercept = 1.0, color = "black", linetype = "dotted"),
                     geom_hline(yintercept = -1.0, color = "black", linetype = "dotted"))

    # make main MA plot
    ma <- ggplot(temp, aes(x = A, y = M)) +
        geom_point(aes(color = candidate, shape = candidate)) +
        scale_y_continuous(paste0("logFC (", y, "/", x, ")")) +
        scale_x_continuous("Ave_intensity") +
        ggtitle(title) + 
        ma_lines
    
    # make separate MA plots
    ma_facet <- ggplot(temp, aes(x = A, y = M)) +
        geom_point(aes(color = candidate, shape = candidate)) +
        scale_y_continuous(paste0("log2 FC (", y, "/", x, ")")) +
        scale_x_continuous("log10 Ave_intensity") +
        ma_lines +
        facet_wrap(~ candidate) +
        ggtitle(str_c(title, " (separated)"))

    # make the plots visible
    print(ma)
    print(ma_facet)
}    

# ========== Scatter plots using ggplot ========================================
scatter_plots <- function(results, x, y, title) {
    # makes scatter-plot DE candidate ggplots
        # results - data frame with edgeR results and some condition average columns
        # x - string for x-axis column
        # y - string for y-axis column
        # title - title string to use in plots
        # returns a list of plots
    
    # 2-fold change lines
    scatter_lines <- list(geom_abline(intercept = 0.0, slope = 1.0, color = "black"),
                          geom_abline(intercept = 0.301, slope = 1.0, color = "black", linetype = "dotted"),
                          geom_abline(intercept = -0.301, slope = 1.0, color = "black", linetype = "dotted"),
                          scale_y_log10(),
                          scale_x_log10())

    # make main scatter plot
    scatter <- ggplot(results, aes_string(x, y)) +
        geom_point(aes(color = candidate, shape = candidate)) +
        ggtitle(title) + 
        scatter_lines

    # make separate scatter plots
    scatter_facet <- ggplot(results, aes_string(x, y)) +
        geom_point(aes(color = candidate, shape = candidate)) +
        scatter_lines +
        facet_wrap(~ candidate) +
        ggtitle(str_c(title, " (separated)")) 

    # make the plots visible
    print(scatter)
    print(scatter_facet)
}

# ========== Volcano plots using ggplot ========================================
volcano_plot <- function(results, x, y, title) {
    # makes a volcano plot
        # results - a data frame with edgeR results
        # x - string for the x-axis column
        # y - string for y-axis column
        # title - plot title string
    
    # uses transformed data
    temp <- transform(results, x, y)
    
    # build the plot
    ggplot(temp, aes(x = M, y = P)) +
        geom_point(aes(color = candidate, shape = candidate)) +
        xlab("log2 FC") +
        ylab("-log10 FDR") +
        ggtitle(str_c(title, " Volcano Plot"))
}

# ============== individual protein expression plots ===========================
# function to extract the identifier part of the accesssion
get_identifier <- function(accession) {
    identifier <- str_split(accession, "\\|", simplify = TRUE)
    identifier[,3]
#    identifier <- accession
}

set_plot_dimensions <- function(width_choice, height_choice) {
    options(repr.plot.width=width_choice, repr.plot.height=height_choice)
}

plot_top_tags <- function(results, nleft, nright, top_tags, color = c("red", "blue")) {
    # results should have data first, then test results (two condition summary table)
    # nleft, nright are number of data points in each condition
    # top_tags is number of up and number of down top DE candidates to plot
    # get top ipregulated
    up <- results %>% 
        filter(logFC >= 0) %>%
        arrange(FDR)
    up <- up[1:top_tags, ]
    
    # get top down regulated
    down <- results %>% 
        filter(logFC < 0) %>%
        arrange(FDR)
    down <- down[1:top_tags, ]
    
    # pack them
    proteins <- rbind(up, down)
        
    color_bars = c(rep(color[1], nleft), rep(color[2], nright))
    for (row_num in 1:nrow(proteins)) {
        row <- proteins[row_num, ]
        vec <- as.vector(unlist(row[1:(nleft + nright)]))
        names(vec) <- colnames(row[1:(nleft + nright)])
        title <- str_c(get_identifier(row$Acc), ", int: ", scientific(mean(vec), 2), 
                       ", FDR: ", scientific(row$FDR, digits = 3), 
                       ", FC: ", round(row$FC, digits = 1),
                       ", ", row$candidate)
        barplot(vec, col = color_bars, main = title,
                cex.main = 1.0, cex.names = 0.7, cex.lab = 0.7)
    }    
}

# ============== CV function ===================================================
CV <- function(df) {
    # Computes CVs of data frame rows
        # df - data frame, 
        # returns vector of CVs (%)
    ave <- rowMeans(df)    # compute averages
    sd <- apply(df, 1, sd) # compute standard deviations
    cv <- 100 * sd / ave   # compute CVs in percent (last thing gets returned)
}

Read the IRS-normalized TMT intensity data¶

We checked the data quality in an earlier notebook, and it looked okay.

In [3]:
# load the IRS-normalized data and check the table
data_import <- read_tsv("labeled_grouped_protein_summary_TMT_9_IRS_normalized.txt", 
                        guess_max = 5854)

# "Filter" column flags contams and decoys
# "Missing" column flags proteins without reporter ion intensities (full sets missing)
# the table from pandas is sorted so the rows we want come first
data_all <- filter(data_import, is.na(Filter), is.na(Missing))
data_sl <- select(data_all, contains("SLNorm_"))
data_irs <- select(data_all, contains("IRSNorm_"))

# save gene names for edgeR so we can double check that results line up
accessions <- data_all$Accession

# see how many rows of data we have
length(accessions)

# set some larger plot dimensions
set_plot_dimensions(9, 9)
Parsed with column specification:
cols(
  .default = col_double(),
  Accession = col_character(),
  Identical = col_character(),
  Similar = col_character(),
  OtherLoci = col_character(),
  Filter = col_character(),
  Missing = col_character(),
  Description = col_character()
)

See spec(...) for full column specifications.

4249

Organize the data frame by biological group¶

In [4]:
# define the groups (after IRS)
E15_irs <- select(data_irs, contains("_E15_"))
E18_irs <- select(data_irs, contains("_E18_"))
P0_irs <- select(data_irs, contains("_P0_"))
P3_irs <- select(data_irs, contains("_P3_"))
P6_irs <- select(data_irs, contains("_P6_"))
P9_irs <- select(data_irs, contains("_P9_"))

# put groups together into a single data frame
tmt_data <- bind_cols(E15_irs, E18_irs, P0_irs, P3_irs, P6_irs, P9_irs)

# Set color vector:
colors <- c(rep("blue", 3), rep("red", 6), rep("dark green", 6), rep("grey", 3))

# make indexes for young and old
E15 <- 1:3
mid <- 4:9
older <- 10:15
P9 <- 16:18

Load data into edgeR data structures¶

We are defining the groups that will be compared explicitly and using all the samples for variance estimates. We put the data into a data frame, grouped by condition. We defined some column indexes for each condition, set some colors for plotting. We can run TMM normalization, check the final intensity distributions, and see how distinct the biological groups are in an MDS cluster view.

In [5]:
# get the biological sample data into a DGEList object
group <- c(rep('E15', 3), rep('mid', 6), rep('older', 6), rep('P9', 3))
y <- DGEList(counts = tmt_data, group = group, genes = accessions)

# run TMM normalization so we have the results in y
y <- calcNormFactors(y)

tmt_tmm <- apply_tmm_factors(y, table = TRUE, color = colors)

# check the clustering
plotMDS(y, col = colors, main = "Data after TMM")
Library size factors:
 IRSNorm_E15_1 -> 1.031476
 IRSNorm_E15_2 -> 1.008094
 IRSNorm_E15_3 -> 0.986400
 IRSNorm_E18_1 -> 1.010526
 IRSNorm_E18_2 -> 1.012694
 IRSNorm_E18_3 -> 0.997802
 IRSNorm_P0_1 -> 0.998726
 IRSNorm_P0_2 -> 0.991580
 IRSNorm_P0_3 -> 0.999595
 IRSNorm_P3_1 -> 0.986234
 IRSNorm_P3_2 -> 0.999611
 IRSNorm_P3_3 -> 1.005907
 IRSNorm_P6_1 -> 0.980859
 IRSNorm_P6_2 -> 0.993474
 IRSNorm_P6_3 -> 1.011724
 IRSNorm_P9_1 -> 0.974078
 IRSNorm_P9_2 -> 1.004781
 IRSNorm_P9_3 -> 1.009580

Trimmed mean of M-values (TMM) factors:
 IRSNorm_E15_1 -> 0.712297
 IRSNorm_E15_2 -> 0.660870
 IRSNorm_E15_3 -> 0.666675
 IRSNorm_E18_1 -> 0.839775
 IRSNorm_E18_2 -> 0.704975
 IRSNorm_E18_3 -> 0.849973
 IRSNorm_P0_1 -> 0.908618
 IRSNorm_P0_2 -> 0.896906
 IRSNorm_P0_3 -> 0.880678
 IRSNorm_P3_1 -> 1.077557
 IRSNorm_P3_2 -> 1.213998
 IRSNorm_P3_3 -> 1.069485
 IRSNorm_P6_1 -> 1.224558
 IRSNorm_P6_2 -> 1.314099
 IRSNorm_P6_3 -> 1.317240
 IRSNorm_P9_1 -> 1.463104
 IRSNorm_P9_2 -> 1.354746
 IRSNorm_P9_3 -> 1.501006

Combined (lib size and TMM) normalization factors:
 IRSNorm_E15_1 -> 0.734718
 IRSNorm_E15_2 -> 0.666219
 IRSNorm_E15_3 -> 0.657608
 IRSNorm_E18_1 -> 0.848614
 IRSNorm_E18_2 -> 0.713924
 IRSNorm_E18_3 -> 0.848105
 IRSNorm_P0_1 -> 0.907461
 IRSNorm_P0_2 -> 0.889354
 IRSNorm_P0_3 -> 0.880321
 IRSNorm_P3_1 -> 1.062723
 IRSNorm_P3_2 -> 1.213527
 IRSNorm_P3_3 -> 1.075802
 IRSNorm_P6_1 -> 1.201120
 IRSNorm_P6_2 -> 1.305523
 IRSNorm_P6_3 -> 1.332683
 IRSNorm_P9_1 -> 1.425177
 IRSNorm_P9_2 -> 1.361223
 IRSNorm_P9_3 -> 1.515386

Groups separate left-to-right¶

The TMM factors range from 0.7 to 1.5 suggesting some large compositional changes across the developmental ages. This testing is mostly a categorization exercise. We will compare the blue group (E15) to the red group (E18 and P0) and compare the red group (E18 and P0) to the dark green group (P3 and P6). The pairwise comparisons may capture some of the major developmental changes. Focusing on just two comparisons may make interpreting the results a little easier.

EdgeR statistical testing starts here¶

Compute the shared variance trend¶

One of the more powerful features of edgeR is computing variances across larger numbers of genes (proteins) to get more robust variance estimates for small replicate experiments. Here, we have 18 samples across all ages to use to improve the variance estimates and reduce false positive differential expression (DE) candidates. We have an edgeR estimateDisp function that does all of this and a visualization function to check the result.

We need to estimate the dispersion parameters before we can do the actual statistical testing. This only needs to be done once. Each exact test will take specified conditions and compare them using the normalization factors and dispersion estimates saved in the DGEList object y.

In [6]:
# compute dispersions and plot BCV
y <- estimateDisp(y)
plotBCV(y, main = "BCV plot of IRS/TMM normalized data")
Design matrix not provided. Switch to the classic mode.

E15 lenses (n=3) versus E18+P0 lenses (n=6)¶

Compare youngest (E15) lenses to mid-age lenses (E18 and P0).

We will specify the pair of interest for the exact test in edgeR and use the experiment-wide dispersion. The topTags table show us the most significant candidates (top 10). We can also summarize how many up and down regulated candidates we have at an FDR of 0.10. We save the test results in tt. We use a handy MA (mean-average) plotting function from limma to visualize the DE candidates and check the p-value distribution.

In [7]:
# use a 0.05 cutoff
tt = pairwise_test(y, c("E15", "mid"), 0.05)
                      genes   logFC logCPM    PValue       FDR
410   sp|P07356|ANXA2_MOUSE  1.2510  8.413 1.485e-19 6.311e-16
848    sp|P62317|SMD2_MOUSE -0.9485  6.796 3.067e-16 4.779e-13
402   sp|Q8BFX1|RN187_MOUSE  2.6494  8.140 3.806e-16 4.779e-13
1188  sp|O35658|C1QBP_MOUSE -0.8486  5.942 4.499e-16 4.779e-13
1374  sp|A2AWP0|BIRC7_MOUSE  1.9059  5.903 1.032e-14 8.771e-12
389    sp|Q64448|CXA3_MOUSE  1.3453  8.097 3.037e-14 2.151e-11
1376  sp|P05132|KAPCA_MOUSE  1.2097  5.591 5.065e-14 3.074e-11
615  tr|D3Z2H9|D3Z2H9_MOUSE -0.7327  7.462 1.989e-13 1.056e-10
166    sp|Q9DCL9|PUR6_MOUSE  0.9877  9.805 4.478e-13 2.114e-10
467   sp|Q91WJ8|FUBP1_MOUSE -0.8942  7.805 1.024e-12 4.352e-10

       mid-E15
Down       492
NotSig    3213
Up         544

We have around 500 DE candidates in each direction¶

There is a nice balance between up (red) and down (blue) candidates (about 500 each) with 3.2K unchanged proteins. Many candidates have abundance levels that are different from the unchanged proteins (black points in the MA plot). The p-value distribution has a typical pattern of two distributions. There is a uniform (flat) distribution of p-values from 0 to 1 from chance associated with unchanged proteins. There is a sharp excess of small p-values that come from the (putative) DE candidates.

Compute averages, flag candidates, and save the pair-wise test results¶

We will add the statistical testing results (logFC, p-values, and FDR), condition intensity averages, and candidate status to the results data frame (which has the TMM-normalized data) and accumulate all comparisons into all_results.

We will make MA plots, scatter plots, and a volcano plot using ggplot2. We will also look at the distribution of log2 expression ratios separated by differential expression (DE) category. The Benjamini-Hochberg corrected edgeR p-values are used to categorize the DE candidates: no > 0.10 > low > 0.05 > med > 0.01 > high.

In [8]:
# get the results summary
results <- collect_results(tmt_tmm, tt$table, E15, "E15", mid, "mid")

# make column names unique by adding comparison (for the accumulated frame)
results_temp  <- results
colnames(results_temp) <- str_c(colnames(results), "_E15_mid")

# accumulate testing results
all_results <- cbind(accessions, results_temp)

Count candidates and look at fold-change distributions¶

In [9]:
# count candidates and check log2 fold change distributions
log2_fold_changes(results, 3, 3, "E15 vs mid-age lenses logFC distributions by candidate")
  candidate    n
1      high  596
2       med  440
3       low  310
4        no 2903

Main summary plots¶

We have many comparisons to visualize, so we will use functions to generate a series of plots. We will make: an MA plot with candidates highlighted by color, faceted MA plots separated by candidate status, a scatter plot with candidates highlighted by color, faceted scatter plots separated by candidate status, and a volcano plot with candidates highlighted by color. The solid black lines in the MA and scatter plots are the 1-to-1 lines; the dotted lines are 2-fold change lines.

MA plots¶

In [10]:
# make MA plots
MA_plots(results, "ave_E15", "ave_mid", "E15 vs mid-age lenses")

The non-candidates and candidates have a typical pattern¶

We see a nice pattern of increasing fold changes as we cut candidates by increasingly stricter FDR (Benjamini-Hochberg corrected p-values) thresholds. This is more easily seen in the facetted plots (the second panel). The 600 "high" candidates (FDR < 0.01) in orange look well-deparated from the unchanged proteins (purple points in lower right).

Scatter plots¶

In [11]:
# make scatter plots
scatter_plots(results, "ave_E15", "ave_mid", "E15 vs mid-age lenses")

Volcano plot¶

In [12]:
# make a volcano plot
volcano_plot(results, "ave_E15", "ave_mid", "E15 vs mid-age lenses")

Check some individual proteins¶

Since we have many candidates, we will look at the top 100 by p-value in each direction.

In [13]:
# look at the top candidates
set_plot_dimensions(9, 3.5)

n_left <- 3
n_right <- 6
top_tags <- 100
color <- c("blue", "red") 

plot_top_tags(results, n_left, n_right, top_tags, color)
set_plot_dimensions(9, 9)

E18 and P0 seem similar¶

The age grouping that emerged from the cumulative abundance distribution curves (and was also apparent in the MDS clustering first dimension separations) results in convincing abundance differences. The combined E18 and P0 ages seem to have consistent protein abundance levels in most of the (red) individual protein bar plots.

Mid-age lenses (n=6) versus older lenses (n=6)¶

Compare mid-age lenses (E18 and P0) to older-age lenses (P3 and P6).

We will specify the pair of interest for the exact test in edgeR and use the experiment-wide dispersion. The topTags table show us the most significant candidates (top 10). We can also summarize how many up and down regulated candidates we have at an FDR of 0.10. We save the test results in tt. We use a handy MA (mean-average) plotting function from limma to visualize the DE candidates and check the p-value distribution.

In [14]:
# use a 0.05 cutoff
tt = pairwise_test(y, c("mid", "older"), 0.05)
                      genes   logFC logCPM    PValue       FDR
234  tr|E9Q616|E9Q616_MOUSE  1.3464  9.289 7.877e-35 3.347e-31
772   sp|Q8K0S2|ERIC5_MOUSE  0.9720  7.402 4.493e-18 9.546e-15
172   sp|Q62318|TIF1B_MOUSE -0.8207  9.319 7.985e-17 8.965e-14
366    sp|Q99MD9|NASP_MOUSE -1.1305  8.174 8.439e-17 8.965e-14
811   sp|Q3ULJ0|GPD1L_MOUSE  0.9674  6.807 8.122e-16 6.902e-13
1057  sp|Q920Q8|NS1BP_MOUSE  0.7774  6.075 2.120e-15 1.501e-12
1191  sp|Q9QYH6|MAGD1_MOUSE  0.7820  6.081 5.392e-14 3.273e-11
6     sp|P23927|CRYAB_MOUSE  1.9323 14.472 1.616e-13 7.715e-11
266   sp|P10107|ANXA1_MOUSE  1.0138  9.252 1.634e-13 7.715e-11
166    sp|Q9DCL9|PUR6_MOUSE  0.7786  9.805 3.349e-13 1.423e-10

       older-mid
Down         603
NotSig      3107
Up           539

We have around 600 DE candidates in each direction¶

There is a nice balance between up (red) and down (blue) candidates (about 600 each) with 3.1K unchanged proteins. Many candidates have abundance levels that are different from the unchanged proteins (black points in the MA plot). The p-value distribution has a typical pattern of two distributions. There is a uniform (flat) distribution of p-values from 0 to 1 from chance associated with unchanged proteins. There is a sharp excess of small p-values that come from the (putative) DE candidates.

Compute averages, flag candidates, and save the pair-wise test results¶

We will add the statistical testing results (logFC, p-values, and FDR), condition intensity averages, and candidate status to the results data frame (which has the TMM-normalized data) and accumulate all comparisons into all_results.

We will make MA plots, scatter plots, and a volcano plot using ggplot2. We will also look at the distribution of log2 expression ratios separated by differential expression (DE) category. The Benjamini-Hochberg corrected edgeR p-values are used to categorize the DE candidates: no > 0.10 > low > 0.05 > med > 0.01 > high.

In [15]:
# get the results summary
results <- collect_results(tmt_tmm, tt$table, mid, "mid", older, "older")

# make column names unique by adding comparison (for the accumulated frame)
results_temp  <- results
colnames(results_temp) <- str_c(colnames(results), "_mid_older")

# accumulate testing results
all_results <- cbind(accessions, results_temp)

Count candidates and look at fold-change distributions¶

In [16]:
# count candidates and check log2 fold change distributions
log2_fold_changes(results, 3, 3, "Mid-age vs Older-age lenses logFC distributions by candidate")
  candidate    n
1      high  718
2       med  424
3       low  285
4        no 2822

Main summary plots¶

We have many comparisons to visualize, so we will use functions to generate a series of plots. We will make: an MA plot with candidates highlighted by color, faceted MA plots separated by candidate status, a scatter plot with candidates highlighted by color, faceted scatter plots separated by candidate status, and a volcano plot with candidates highlighted by color. The solid black lines in the MA and scatter plots are the 1-to-1 lines; the dotted lines are 2-fold change lines.

MA plots¶

In [17]:
# make MA plots
MA_plots(results, "ave_mid", "ave_older", "Mid-age vs older-age lenses")

The non-candidates and candidates have a typical pattern¶

We see a nice pattern of increasing fold changes as we cut candidates by increasingly stricter FDR (Benjamini-Hochberg corrected p-values) thresholds. This is more easily seen in the facetted plots (the second panel). Again, the 700+ orange points (upper left) seem well-separated from the unchanged proteins in purple (lower right).

Scatter plots¶

In [18]:
# make scatter plots
scatter_plots(results, "ave_mid", "ave_older", "Mid-age vs older-age lenses")

Volcano plot¶

In [19]:
# make a volcano plot
volcano_plot(results, "ave_mid", "ave_older", "Mid-age vs older-age lenses")

Check some individual proteins¶

Since we have many candidates, we will look at the top 100 by p-value in each direction.

In [20]:
# look at the top candidates
set_plot_dimensions(9, 3.5)

n_left <- 6
n_right <- 6
top_tags <- 100
color <- c("red", "dark green") 

plot_top_tags(results, n_left, n_right, top_tags, color)
set_plot_dimensions(9, 9)

Top candidates look convincing¶

The age grouping that emerged from the cumulative abundance distribution curves (and was also apparent in the MDS clustering first dimension separations) results in convincing abundance differences. The combined mid and older ages seem to have consistent protein abundance levels in most of the individual protein bar plots. There are some exceptions, of course, such as beta B2 and alpha B. A few of the major lens proteins are expressed after birth.

This lens time course is simpler than most. We have three major cell populations: epithelial cells (monolayer on the anterior face of the lens), differentiating cortex cells (the outer layers of the lens (elongating cells with nuclei)), and the mature fiber cells (bags of crystallins and other major lens proteins). There is little protein turn over in lens and it is well known that crystallins progressively dominate the proteome as the lens matures.

Summary¶

This simplified way to look at a time course experiment is not very elegant. It relies on an assumption that protein changes will be increasing with age, decreasing with age, or (because of how we normalized the data (IRS + TMM)) relatively flat with age. The fold-changes between younger and older lenses would probably be sufficient for categorization. The statistical testing might help with validating changes.

There will be some lens growth during the developmental ages. If we think about the lens as a sphere, epithelial cells grow like the surface area (R^2) and mature fiber cells grow like the volume (R^3). Major lens proteins should go up across the ages, epithelial cell proteins should go down. Proteins from the cortex probably go down, but none of these cell populations are homogeneous. Epithelial cells at the start of differentiation will be more typical cells. Mature fiber cells are mostly major lens proteins with regular cell machinery and structures degraded. The cortex cells span this spectrum.

Save the all_results frame to TSV file¶

We collected the normalized data and statistical testing results and we can save that table to make a nice spreadsheet summary.

In [21]:
write.table(all_results, "Khan-2018_results_3-age-groups_edgeR-exact_TMM.txt", sep = "\t",
           row.names = FALSE, na =  " ")

Log the session information¶

In [22]:
sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] psych_2.0.7     edgeR_3.24.3    limma_3.38.3    scales_1.1.1   
 [5] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4    
 [9] readr_1.3.1     tidyr_1.1.1     tibble_3.0.3    ggplot2_3.3.2  
[13] tidyverse_1.3.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5       locfit_1.5-9.4   lubridate_1.7.9  lattice_0.20-41 
 [5] assertthat_0.2.1 digest_0.6.25    IRdisplay_0.7.0  R6_2.4.1        
 [9] cellranger_1.1.0 repr_1.1.0       backports_1.1.8  reprex_0.3.0    
[13] evaluate_0.15    httr_1.4.2       pillar_1.4.6     rlang_1.0.2     
[17] uuid_0.1-4       readxl_1.3.1     rstudioapi_0.11  blob_1.2.1      
[21] labeling_0.3     splines_3.5.3    munsell_0.5.0    broom_0.7.0     
[25] compiler_3.5.3   modelr_0.1.8     pkgconfig_2.0.3  base64enc_0.1-3 
[29] mnormt_1.5-6     htmltools_0.4.0  tidyselect_1.1.0 crayon_1.3.4    
[33] dbplyr_1.4.4     withr_2.2.0      grid_3.5.3       nlme_3.1-148    
[37] jsonlite_1.7.0   gtable_0.3.0     lifecycle_0.2.0  DBI_1.1.0       
[41] magrittr_1.5     cli_3.3.0        stringi_1.4.6    farver_2.0.3    
[45] fs_1.5.0         xml2_1.3.2       ellipsis_0.3.2   generics_0.0.2  
[49] vctrs_0.4.1      IRkernel_1.1.1   tools_3.5.3      glue_1.6.2      
[53] hms_0.5.3        parallel_3.5.3   colorspace_1.4-1 rvest_0.3.6     
[57] pbdZMQ_0.3-3     haven_2.3.1     
In [ ]: